Solar Power Prediction Using Insolation Segmentation
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Solution Overview
Problem
Existing methods for predicting solar power generation struggle to accurately account for uncertainties in meteorological and electronic factors, leading to inaccuracies in peak cut loss calculations and overall power generation predictions.
Innovation Solution
An electric power generation prediction method based on expected value calculation, which involves determining insolation and insolation probability per unit time, and using power generation characteristic data to calculate minimum output power and capacity, allowing for conditional branching in power generation predictions to accurately reflect insolation influences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional average value method is used for insolation determination, then calculation complexity is reduced, but power generation prediction accuracy deteriorates due to inability to account for peak cut loss and meteorological uncertainties
Solution Approach 1:
The patent segments the continuous insolation range into three distinct ranges (first range with zero power generation, second range with proportional power generation, third range with constant power generation at capacity). This segmentation allows the system to account for peak cut loss by treating different insolation conditions differently, improving prediction accuracy while maintaining manageable calculation complexity through structured conditional logic.
2Measurement precision
If detailed conditional branching calculation is performed considering meteorological and electronic factors, then power generation prediction accuracy is improved, but calculation cost and processing time increase
Solution Approach 1:
The patent changes the parameter representation by determining insolation probability per unit time for each segmented range rather than performing continuous complex calculations. This parameter transformation allows the system to capture the effects of meteorological factors and electronic characteristics (including peak cut loss) while reducing computational burden through discrete probability-based calculations.
3Measurement precision
If comprehensive insolation data and probability distribution are maintained, then prediction accuracy is improved, but database load and storage requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for prediction by determining insolation probability per unit time for each segmented range, rather than storing and processing complete continuous insolation data distributions. This extraction approach maintains prediction accuracy by preserving the probabilistic characteristics while significantly reducing database storage requirements and load.
Data Source
AI summary
Accurately predicting a power generation of a natural energy power generation device that is affected by a peak cut loss. A processor of a computer is caused to execute: an insolation analysis step and a power generation prediction step. The power generation prediction step determines a first insolation that is the insolation required to obtain a first power generation that is zero and the minimum output power, and a second power generation that is the capacity. The power generation prediction step also determines a second insolation that is the insolation in a case where the capacity is satisfied. In a case where the insolation is lower than the first insolation, the power generation prediction step determines the first power generation as the power generation, and in a case where the insolation exceeds the second insolation, the power generation prediction step determines the second power generation as the power generation.